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Loss of myeloid Tsc2 predisposes to angiotensin II-induced aortic aneurysm formation in mice

Rationale Genetic studies have proved the involvement of Tuberous sclerosis complex subunit 2 (Tsc2) in aortic aneurysm. However, the exact role of macrophage Tsc2 in the vascular system remains unclear. Here, we examined the potential function of macrophage Tsc2 in the development of aortic remodeling and aortic aneurysms. Methods and results Conditional gene knockout strategy combined with histology and whole-transcriptomic analysis showed that Tsc2 deficiency in macrophages aggravated the progression of aortic aneurysms along with an upregulation of proinflammatory cytokines and matrix metallopeptidase-9 in the angiotensin II-induced mouse model. G protein-coupled receptor 68 (Gpr68), a proton-sensing receptor for detecting the extracellular acidic pH, was identified as the most up-regulated gene in Tsc2 deficient macrophages compared with control macrophages. Additionally, Tsc2 deficient macrophages displayed higher glycolysis and glycolytic inhibitor 2-deoxy-D-glucose treatment partially attenuated the level of Gpr68. We further demonstrated an Tsc2-Gpr68-CREB network in macrophages that regulates the inflammatory response, proteolytic degradation and vascular homeostasis. Gpr68 inhibition largely abrogated the progression of aortic aneurysms caused by Tsc2 deficiency in macrophages. Conclusions The findings reveal that Tsc2 deficiency in macrophages contributes to aortic aneurysm formation, at least in part, by upregulating Gpr68 expression, which subsequently drives proinflammatory processes and matrix metallopeptidase activation. The data also provide a novel therapeutic strategy to limit the progression of the aneurysm resulting from Tsc2 mutations.

INTRODUCTION
Aortic aneurysm (AA) is a life-threatening aortic disease, characterized by a progressive dilatation of the aorta. A majority of AAs are located at two distinct aneurysm-prone regions, the abdominal aorta and thoracic aorta involving the ascending aorta. Smallsized AAs are mostly asymptomatic, whereas large-sized AAs often lead to aortic rupture and sudden death when the diameters are more than 6.0 cm in thoracic aortic aneurysm (TAA) and more than 5.0 cm in abdominal aortic aneurysm (AAA) [1,2]. Although great progress has been achieved in surgical techniques including endovascular aneurysm repair, there is still no effective pharmacological treatments for patients with small AAs or contradictions to surgery [3]. Thus, it is important to expand our knowledge of the mechanisms involved in AAs development and progression.
Although TAA and AAA have different aetiologies and pathogeneses, accumulating experimental evidence propose the strong role of a chronic immune/inflammatory process in both TAA and AAA evocation and progression [3,4]. Likewise to AAA, histological studies demonstrate the presence of large amounts of inflammatory cell infiltrates, particularly macrophages, in the media and adventitia of thoracic aortic wall [4]. Infiltrating macrophages contribute to the degradation of extracellular matrix and death or dysfunction of vascular smooth muscle cells (SMCs) by releasing a range of proteolytic enzymes, including matrix metalloproteinases (MMPs), oxidation-derived free radicals and proinflammatory cytokines (TNF-α, IL-1β, and IL-6), which induce arterial wall destruction and subsequent aorta dilatation [3,4].
Tuberous sclerosis complex subunit 1 (Tsc1) and 2 (Tsc2), which encode the proteins hamartin and tuberin, respectively, form a heterodimeric complex. Tsc1 enhances Tsc2 function by stabilizing Tsc2. Tsc complex participates in a wide range of cellular functions and acts as a tumor growth suppressor [5]. Studies have shown that Tsc2 is a catalytic GTPase-activating protein (GAP) and directly inhibits Rheb small GTPase upstream of mTOR [6,7]. Inactivating mutations in Tsc1 or Tsc2 give rise to the tuberous sclerosis. Patients with tuberous sclerosis have a higher incidence of AAs compared with the general population [8]. However, the underlying molecular mechanisms are poorly understood. Recent studies have reported the role of smooth muscle Tsc1/Tsc2 in the pathogenesis of AAs [9,10]. Tsc1 deletion in smooth muscle cells results in a degradative smooth muscle cell phenotype and progressive TAA [9]. In addition, descending aortic aneurysm is associated with subintimal smooth muscle cell proliferation in a child with a Tsc2 mutation [10]. Whether the deficiency of macrophage Tsc2 is directly involved in aneurysm formation is still a mystery. Emerging evidence indicates that macrophage Tsc2 orchestrates inflammatory response, implying that macrophage Tsc2 may be closely related to vascular homeostasis [11,12]. Thus, we aimed to evaluate the role of macrophage Tsc2 in vascular inflammation and aneurysm pathogenesis.
In this study, we demonstrate that myeloid Tsc2 depletion increased susceptibility to AAs formation and elastin degradation in the angiotensin II-induced mouse model. An evaluation for the underlying mechanism of exacerbating vascular inflammation and extracellular matrix remodeling in myeloid-specific Tsc2 deficient mice leads to the identification of the macrophage G proteincoupled receptor 68 (Gpr68) as a key molecule involved in AAs formation.

RESULTS
Myeloid-specific deletion of Tsc2 exacerbates AngII (angiotensin II) infusion-induced aneurysm development Animals with myeloid-specific disruption of Tsc2 were sacrificed and their aortas compared with controls. Conditional myeloid deletion of Tsc2 in an ApoE -/background induced swollen paws and tails ( Fig. S1A and B), which was accordance with a previous report [11]. However, Tsc2 MKO ApoE -/mice are viable and fertile with uncompromised vasculature development. Systemic infusion of AngII via osmotic mini-pumps into ApoE -/mice for 4 weeks has been repeatedly demonstrated to cause aneurysmal pathology in both thoracic and abdominal aortas [13]. Subsequently, to investigate whether macrophage Tsc2 is crucial to maintain vasculature homeostasis, we crossed myeloid-specifc Tsc2 MKO with hyperlipidemic ApoE -/mice and established AngII-induced murine aortic aneurysm model. The absence of Tsc2 expression on myeloid cells in Tsc2 MKO ApoE -/mice was confirmed by RT-PCR and western blot analysis in peritoneal macrophages ( Fig. S1C and D).
After 4 weeks of AngII (1000 ng/kg/min) infusion, systolic blood pressure increased similarly in conscious Tsc2 MKO ApoE -/and Tsc2 WT ApoE -/mice, but no significant difference in body weight between the two groups was observed (Table S1). Morphologically, the aortas of saline-infused Tsc2 MKO ApoE -/mice did not differ from those of saline-infused controls (Fig. 1A). No spontaneous aortic dilations and aneurysms were observed in the saline-infused mice (Fig. 1A). Although AngII treatment resulted in vascular expansion in both genotypes, Tsc2 MKO A-poE -/mice exhibited significant increases in the maximal descending thoracic and abdominal aortic diameter compared to Tsc2 WT ApoE -/mice ( Fig. 1A-C). As well, both the incidence of AAA and TAA were markedly increased in Tsc2 MKO ApoE −/− mice (83.3% and 55.6%, respectively) than in Tsc2 WT ApoE -/mice (47.1% and 11.8%, Fig. 1B, Table S2).
Myeloid-specific deletion of Tsc2 promotes AngII infusion-induced aortic structural and composition changes Elastin fragmentation is considered crucial for aortic wall dilation and can be detected by Verhoeff-Van Giessen (VVG) staining. Histological analysis of the suprarenal aorta with VVG staining showed that elastin fibers were disrupted in AngII-infused arteries ( Fig. 2A, B). No obvious elastin breakage was identified in aortic sections from saline-infused Tsc2 MKO ApoE −/and Tsc2 WT ApoE -/mice. Notably, disruption and degradation of medial elastic lamina were more severe in AngII-infused Tsc2 MKO ApoE -/mice compared with Tsc2 WT ApoE -/mice ( Fig. 2A, B).
We then checked the expression of several proinflammatory cytokines and matrix metalloproteinase-9 (Mmp9), which have been repeatedly demonstrated to contribute to the pathogenesis of AAs in the aortas [14]. Tsc2 deletion in myeloid cells significantly increased the expression of IL-6 and IL1-β but not MCP-1 and Mmp9 expression in the saline-infused aortas compared with control mice (Fig. 2C). In addition, AngII augmented the expression of proinflammatory cytokines IL-6, IL1-β and MCP-1 in the presence as well as in absence of Tsc2 ( Fig.  2C and D). Consistently, the aortas from Tsc2 MKO ApoE −/− mice infused with AngII displayed a marked upregulation of Mmp9 expression ( Fig. 2C and D). Furthermore, double immunofluorescence analysis detected increased F4/80 + macrophages infiltrating into aortas expressed IL1-β in the aortas from AngII-infused Tsc2 MKO ApoE −/− mice (Fig. 2E).
Moreover, Tsc2 deletion in myeloid cells decreased the relative contents of collagen and smooth muscle cells but augmented macrophage infiltration in the aortic wall, as determined by Masson's trichrome staining and immunostaining (Fig. 3A). As proinflammatory macrophages could enhance the phenotypic changes of smooth muscle cells [15] and thus aggravate aortic aneurysm development [16], we performed co-immunostaining of mouse abdominal aortic tissues. As revealed by immunofluorescence staining ( Fig. 3B and C), Tsc2 deletion in myeloid cells significantly decreased the expression of contractile SMC phenotype marker sm22α but increased the synthetic phenotype marker vimentin in aneurysm tissues. These observations would indicate that the lack of myeloid Tsc2 could aggravate AngII-induced aortic inflammatory responses and structural remodeling.
Identification of Gpr68 up-regulation in Tsc2 deficient macrophages following AngII infusion To clarify the mechanism whereby aneurysm development was exaggerated in AngII-infused Tsc2 MKO ApoE -/mice, we first identified the genes that are regulated by Tsc2 in macrophages following AngII infusion. We performed RNA-Seq analysis on the peritoneal macrophages from Tsc2 MKO ApoE -/mice versus Tsc2 WT ApoE -/infused with AngII for 7 days. As a result, 488 genes were found to be differentially regulated by Tsc2 inactivation (twofold change in expression level; P < 0.05) (Fig. 4A, Table S3). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted on the genes related to Tsc2 expression and revealed that the genes were mainly enriched in several inflammation-related signaling pathways, such as cytokine-cytokine receptor interaction, the TNF signaling pathway and IL-17 signaling (Fig. 4B). Multiple pathways relevant to inflammation are activated in macrophages from Tsc2 MKO ApoE -/mice.
inflammatory cytokines [17], was identified as the most up-regulated gene in Tsc2 deficient macrophages compared with control macrophages ( Fig. 4C and D). A database inquiry indicated that Gpr68 was expressed ubiquitously, with relatively high expression in lipopolysaccharide-induced pro-inflammatory macrophages (Fig.  S2A). These changes in the mRNA expressions were further confirmed by RT-PCR analysis (Fig. 4E). To investigate the potential mechanisms underlying Tsc2-mediated macrophage inflammation in vivo during aortic aneurysm formation, we isolated aortic macrophages from digested whole aorta using a magneticactivated cell sorting system on day 28 from mice infused with AngII as previously described [18,19]. Consistent with the studies in peritoneal macrophages, Gpr68 and proinflammatory cytokines (IL-6, IL-1β, MCP-1) expression were increased in aortic CD11b + myeloid cells from AngII-infused Tsc2 MKO ApoE -/mice in comparison to controls (Fig. S2B). Taken together, these findings from RNA-seq substantiate a crucial role for Tsc2 in regulating inflammatory phenotype of macrophages.

Myeloid-specific deletion of Tsc2 induces Gpr68 expression in AngII-infused aortas
To further unravel the potential contribution of Tsc2 deletion in the aortic response to AngII, Gpr68 expression was detected in AngII-infused aortas. Immunoblotting and RT-PCR analysis confirmed the upregulation of Gpr68 in aortas from AngII-infused Tsc2 MKO ApoE -/mice compared with control mice (Fig. 5A and B). Furthermore, increased F4/80 + macrophages infiltrating into aortas expressed Gpr68 in Tsc2 MKO ApoE -/mice, as revealed by immunofluorescence staining (Fig. 5C). These results suggest that myeloid-specific deletion of Tsc2 induces high Gpr68-expressing macrophages in AngII-infused aortas.
We next examined how Tsc2 regulates the expression of Gpr68 in macrophages. As Gpr68 responds to extracellular acidosis, we measured the cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) in the peritoneal macrophages from AngII-infused mice. Consistent with the increased expression of Gpr68, Tsc2 deficient macrophages displayed higher glycolysis and mitochondrial respiration, as evidenced by increased ECAR and OCR ( Fig. S3A and B). Compared with wildtype macrophages, Tsc2 deficient macrophages showed significantly increased migration (Fig. S3C). Of note, 2-deoxy-D-glucose (2-DG, 2 mg/mL) treatment partially attenuated the expression of Gpr68, indicating that this increased Gpr68 expression seemed to be mediated partially by the enhanced glycolytic activity induced by Tsc2 deficiency (Fig. S3D).
Downregulation of Gpr68 alleviates the expression of inflammatory cytokines and Mmp9 in Tsc2 deficient macrophages To further detect the role of elevated expression of Gpr68, macrophages from AngII-infused Tsc2 MKO ApoE −/− mice were transfected with shRNA-Gpr68 or negative control shRNA (shRNA-NC) for 24 h. As shown in Fig. 6A, Gpr68 inhibition decreased the expression of proinflammatory cytokines IL-6, IL-1β and MCP-1. In addition, Gpr68 inhibition significantly suppressed Mmp9 expression and activity, as determined by western blot and zymography ( Fig. 6B-D).
Gpr68 reportedly can couple to heterotrimeric G proteins and mediates the cAMP-PKA (protein kinase A)-CREB (cAMP response element-binding protein) pathway [20]. CREB functions as a transcription factor that can specifically bind to the cAMP response element (CRE) present in the promoter regions of downstream target genes [21]. Positive immunostaining of phosphorylated CREB has been observed in macrophages of AAs tissues [22], and the modulation of CREB on Mmp9 expression in macrophages has been reported [23], as well. Additionally, recent study has shown that CREB promotes transactivation of Fos gene family (Fosb, Fosl1, c-Fos) to enhance macrophage-mediated inflammation by directly binding to the promoters of these genes [24]. CREB also modulates the expression of TSP-1 [25], a member of the matricellular thrombospondin protein family which plays a key role in the regulation of macrophage inflammatory response and gelatinase activity during the pathogenesis of AAs [26]. We then further investigated the role of Gpr68 in the regulation of CREB activity. In macrophages from AngIIinfused Tsc2 MKO ApoE −/− mice, the level of phosphorylated CREB were increased than in macrophages from AngII-infused Tsc2 WT ApoE −/− mice (Fig. 6E). Gpr68 inhibition reduced the level of phosphorylated CREB (Fig. 6E). Morever, CREB bound to the promoter region of Mmp9 and TSP-1 in murine bone marrow-derived macrophages according to the ChIP assay (Fig. 6F, source data obtained from GEO data set, GSM2663863). These data suggest that Tsc2 may mediates gene transcriptional activation through Gpr68/ CREB pathway.
Downregulation of Gpr68 prevents aneurysm formation induced by AngII administration in Tsc2 MKO ApoE −/− mice To investigate whether high Gpr68-expressing macrophages are involved in the exacerbation of AngII-induced AAs development, Tsc2 MKO ApoE −/− mice were randomized to receive shRNA-Gpr68 or shRNA-NC before AngII pump implantation. Gpr68 inhibition resulted in a marked reduction in AAs incidence and diameter ( Fig. 7A-C, Table S2) without affecting blood pressure levels (Table  S1). Histological analysis showed that aortas from shRNA-Gpr68 treated mice exhibited a moderate change in aortic architecture with reduced elastic fiber fragmentation (Fig. 7D). These observations indicate that the lack of myeloid Tsc2 could aggravate AngIIinduced AAs formation by augmenting Gpr68-mediated macrophage inflammatory response and macrophage-derived Mmp9 expression.

DISCUSSION
Aortic aneurysm is a severe cardiovascular disease with a high risk of mortality related to rupture. There is an urgent need to discover effective pharmacological agents to decelerate or reverse the progression of AAs. The present study demonstrates that myeloid Tsc2 defciency aggravates AAs formation in vivo and that Gpr68 inhibition limits aneurysm enlargement via regulation of CREB phosphorylation, indicating that pharmacological modulation of Gpr68 may be a feasible approach for the treatment of AAs. Fig. 4 Identification of target genes regulated by Tsc2 with genome-wide RNA-seq analysis. A Heat map displayed the top genes with altered expression (|log2FC | >1) in the peritoneal macrophages from Tsc2 MKO ApoE -/mice versus Tsc2 WT ApoE -/infused with AngII for 7 d. B KEGG pathway enrichment analysis was conducted. C Valconoplot shows genes whose expression was significantly altered upon Tsc2 deletion. Blue plots represented significant positive-regulated genes and the red plots for negative-regulated genes. D Heat map of the selected genes from (A). E Quantitative real-time PCR were performed to evaluate mRNA expression of the indicated genes (n = 5). *P < 0.05, vs. macrophages from Tsc2 WT ApoE -/mice infused with AngII.
One major finding of this study is that Tsc2 deletion in macrophages exacerbated aortic aneurysms formation. It is interesting to observe that Tsc2 deletion markedly increased the the maximal descending thoracic and abdominal aortic diameter. Our study is in line with a previous study which showed that mutations in the Tsc2 gene result in descending aortic aneurysm. Although TAA and AAA have different aetiologies and pathogeneses, accumulating evidence supports the important role of a chronic inflammatory process in both TAA and AAA development [2]. Macrophages, the most abundant immune cells in the aneurysmal arterial wall, are critical mediators of aneurysm evocation and progression. Tsc2 deletion in myeloid cells induced increases in inflammatory response, macrophage infiltration and Mmp9 expression, which subsequently contribute to rupture of elastin fibers and aortic wall dilation. Tsc2 deletion induced proaneurysmal phenotype was consistent with the clinical studies that patient with Tsc2 mutation lead to aortic aneurysms formation [10,27]. Our RNAseq data showed that Tsc2 deficient macrophages upregulated the expressions of inflammatory genes (including IL-6, IL-1β, MCP-1, Ccl7, Ccl12) and MMP genes (Mmp9, Mmp10, Mmp13) in the presence of AngII. Actually, mixed findings have been reported on the role of genetic manipulation of the Tsc1/Tsc2 in the production of inflammatory and immunomodulatory cytokines. For example, Pan et al [28]. demonstrated that Tsc1 deficient macrophages promotes the expression of proinflammatory cytokines in response to TLR stimulation due to increased activation of mTORC1. Moreover, Tsc1 knockout bone marrow-derived macrophages treated by lipopolysaccharide secreted more of the pro-inflammatory cytokines TNF-α and IL-6 but less of the anti-inflammatory cytokine IL-10 [29]. Zhu et al [12]. reported that Tsc1/2 complex inhibited M1 polarization in macrophages and that myeloid cell-specific Tsc1 knockout mice spontaneously developed an inflammatory disorder. Using Tsc2-deficient cells, including human angiomyolipoma parental cells and mouse embryonic fibroblasts (MEFs), Wang et al [30]. found that deletion of Tsc2 up-regulated IL-6 transcription and secretion compared to control cells. However, a research group reported that Tsc2 deficiency diminished the production of proinflammatory cytokines in MEFs and human monocytes [31]. The reasons for this inconsistency possibly due to the different cell types, context-dependent actions of macrophage Tsc1/2 and duration of Tsc1/2 inhibition/deletion used in these studies. This would require further clarification in future research.
Another major finding of this study is that elevated expression of Gpr68 in macrophages is responsible for the exacerbated aneurysms formation. RNA-Seq analysis identified Gpr68 (also known as Ogr1) as the most up-regulated gene in Tsc2 deficient macrophages. We also found elevated expression of Gpr68, particularly in macrophages from myeloid Tsc2 deficient aortas. Intriguingly, Gpr68 inhibition significantly suppressed the expression of proinflammatory cytokines and Mmp9 activity in Tsc2 deficient macrophages. In vivo, Gpr68 inhibition resulted in a marked reduction in AAs incidence and diameter. Gpr68 is activated by the extracellular acidic pH. Nevertheless, high-Gpr68-expressing macrophages were also found in inflammatory site and exerted pro-inflammatory effects in vivo and in vitro [17]. A database inquiry indicated that Gpr68 was mainly expressed in immune cells, including T cells and macrophages ( Supplementary Fig. 2A). The expression of Gpr68 was significantly increased in lipopolysaccharide-induced pro-inflammatory macrophages (Supplementary Fig. 2A). High-Gpr68-expressing monocytes released more TNFα and IL-6 in response to lipopolysaccharide stimuli under natural conditions, and the lipopolysaccharide stimulated release of inflammatory cytokines was further increased under acidic conditions [17]. Moreover, Gpr68 inhibition in monocytes alleviates CKD induced cardiac inflammation [17]. Gpr68 deficient mice exhibit less severe airway inflammation in the ovalbumin-induced experimental asthma model [32]. These observations highlighted the importance of manipulation of Gpr68 in the regulation of macrophage-mediated inflammation.
More interestingly, glycolytic inhibitor 2-DG treatment partially attenuated the level of Gpr68, indicating that Tsc2 deficiency negatively regulates Gpr68 expression involving activation of glycolysis. Recent studies demonstrated a link between inflammation and glycolysis in the pathogenesis of aortic aneurysm [33,34]. Enhanced glycolytic activity in aortic wall as well as in macrophages contributes to the pathogenesis of aneurysm development [35]. Tsc2 has been reported to mediate glycolysis in several diseases [5,36]. The end point product of glycolysis is lactate that leads to extracellular acidosis. Under ischemic and inflammatory circumstances, such as ischaemic heart disease, atherosclerotic lesions, and rheumatoid arthritis, extracellular acidification occurs due to the stimulation of anaerobic glycolysis [37][38][39]. Using a microelectrode, Naghavi et al [38]. found pH values could be as low as 6.8 in the subendothelial areas of human carotid plaques. In addition, visualization of the atherosclerotic lesions with two pH-sensitive fluorescent dyes indicated that the pH may reach values even below 6 [38]. Importantly, a pH-sensitive probe pHrodo together with fluorescence reflectance imaging identified acidic regions at positions of macrophage accumulation in human and mouse abdominal aortic aneurysm lesions [40]. Gpr68-family GPCRs sense neutral or weak pH 6-8 through histidine residues [41]. Therefore, we speculate that Tsc2 deficiency-induced glycolysis and subsequent extracellular acidic pH mediates, at least partly, the upregulation of Gpr68 expression in the aorta of mice. On the basis of our present results, it will be an interesting future issue to measure pH in aneurysm tissues. Gpr68 is also expressed in endothelial cells of mammalian small-diameter blood vessels, which sense shear stress [42]. The high-Gpr68expressing macrophages that infiltrated into the aorta may have been exposed to shear stress around vessels and were activated to produce inflammatory cytokines.
The protective effect of Gpr68 blockade might be due to modulation of phosphorylated CREB. Recent study by Cui et al [22]. revealed that the levels of phosphorylated CREB (p-CREB) in macrophages were increased significantly in AAD and phosphorylation of CREB by p38α promotes inflammatory reactions of macrophages. Additionally, CREB promotes transactivation of Fos gene family (Fosb, Fosl1, c-Fos) to enhance macrophage-mediated inflammation [24]. We observed that Gpr68 inhibition reversed the elevated level of phosphorylated CREB induced by lacking Tsc2 in macrophages. Of interest, increased expression of multiple genes in Tsc2 deficient macrophages including Mmp9, Fosl1, Fos and TSP-1 were involved as the downstream targets of CREB and associated with aneurysms formation. Therefore, Tsc2 may regulate inflammatory and proteolytic genes through Gpr68/CREB pathway. We developed a mechanistic model describing the role of macrophage Tsc2 in AAs formation (Fig. 8).
We acknowledge that there are limitations in our study. Lyz2cre leads to deletion of Tsc2 in monocyte/macrophages, granulocytes, and few CD11c + dendritic cells [43]. Tsc2 may affect neutrophil function and thus contribute to aneurysm formation. Macrophage-targeted delivery vectors (including nano-delivery strategy) instead of shRNA lentiviral particles may be helpful for the treatment of aortic aneurysm in future study. The current study is also limited by the animal model. There is still no single animal model that could reflect the full disease spectrum of human aortic aneurysms. Further conditional Tsc2 knockout mice in other vascular cell types, as well as aortic aneurysms models will be more informative.
In summary, our data demonstrate that Tsc2 deletion triggers a proinflammatory phenotype in macrophages through the Gpr68/ CREB pathway, thereby aggravating aortic remodeling and aortic aneurysm formation. Targeting macrophage Gpr68 may represent a novel potential therapeutic approach to limit aortic aneurysm expansion. Therefore, small molecule agents could be designed to block macrophage Gpr68 signaling to prevent aortic aneurysm development especially in patient with Tsc2 mutation.
Genotyping was performed by PCR analysis according to The Jackson Laboratory's protocol. For Gpr68 inhibition, shRNA lentiviral particles carrying Gpr68 gene silencing sequence (5ʹ-UAACAAUUCAGGUUC-CUCGCCCUGG-3ʹ) or negative control (sh-NC) were synthesized as  previously described [44] and purchased from BoShang Bio-tech Company (Jinan, China). sh-Gpr68 or sh-NC (2 × 10 7 TU per mouse) was injected into the mice through the tail vein before AngII infusion. Randomization and allocation concealment were performed. All experimental protocols conformed to the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health and were approved by the animal ethics committee of Shandong University.

Determination of blood pressure
Systolic blood pressure (SBP) was measured noninvasively on conscious mice using a noninvasive tail-cuff system (Natsume, Tokyo, Japan). Measurements were recorded at the same time of day throughout the study. Systolic blood pressure was measured at least five times, and the mean was used for each mice.

Angiotensin II infusion
To induce aortic aneurysms formation, all mice received angiotensin II (1000 ng/kg/min) or saline by micro-osmotic pumps (Alzet model 2004; Durect, Cupertino, CA, USA) as we previously described [45]. All mice received regular laboratory chow diet throughout the study. Mice that died immediately after the osmotic pumps implantation were excluded from the present study. At 28 days after the osmotic pumps implantation, mouse aortas were harvested, and aneurysms were quantified. We measured the diameter of the ascending, descending thoracic, and abdominal aortic segments of the extracted aortas. An aneurysm was defined as ≥50% enlargement of the external diameter compared to aortas from saline-infused control mice. Necropsy was performed to confirm aortic rupture during AngII infusion. Operators responsible for the experimental procedures and data analysis were blinded to group allocation. The number of animals that received AngII infusion was determined based on prior experimentation in our lab.

Histology and immunohistochemistry
Paraffin-embedded aortic sections (5 μm thick) were subjected to hematoxylin and eosin (H&E) and Verhoeff-Van Gieson Elastin staining (Sigma-Aldrich) for morphological assessment and detection of elastic fiber integrity, respectively. For immunohistochemical staining, sections were incubated with primary antibodies overnight at 4°C, followed by secondary antibody before visualization with diaminobenzidine (Gene Tech, Shanghai, China). In the double immunofluorescence analysis, appropriate secondary antibodies (Alexa Fluor 488 and 594) were employed. DAPI was used for nuclei staining. Images were observed with a fluorescence microscope (Olympus, Tokyo, Japan).

Cell cultures
Three days before collecting peritoneal macrophages, mice were intraperitoneally injected with 1 mL of 3.0% thioglycollate medium. Cells were further perfused from the peritoneal cavity and then cultured in RPMI 1640 medium containing 10% fetal bovine serum (FBS).

Assays of oxygen consumption rate and extracellular acidification rate
Peritoneal macrophages were seeded at 4×10 4 cells per well in Seahorse XF 96-well culture microplates. The cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were assessed using the Seahorse XFe 96 Extracellular Flux Analyzer (Agilent Technologies, CA, USA) according to the manufacturer's instructions. OCR and ECAR were determined using Seahorse XF Cell Mito Stress Test Kit and Seahorse XFe Glycolysis Stress Test Kit, respectively. Results were collected with Wave software (Agilent Technologies, CA, USA). OCR is indicated in pmol/minute and ECAR in mpH/minute.

Transwell migration assay
Bone marrow-derived macrophages (BMDMs) were isolated from long bones and washed with PBS. Cells were starved 24 h prior to assay set up. 1×10 4 cells/200 μl were suspended in medium without FBS and seeded into the upper chamber of transwell plates (Corning, USA) with 8 μm pore filters. The lower chamber was added with complete medium (containing 10% FBS). Following 12 h incubation at 37°C, cells that migrated to the lower surface were stained with crystal violet. Migrated cells per field was quantified under a microscope.

Real-time polymerase chain reaction (RT-PCR)
Total RNAs were extracted using TRIzol reagent (Invitrogen) and reversetranscribed into cDNA using the PrimeScript reverse transcriptase reagent kit (Takara, Japan). All samples were amplified using SYBR Green-based RT-PCR. The mRNA levels were normalized to those of β-actin. Primer sequences for real-time quantification are listed in Table S4.

Western blot assay
Total proteins were extracted from cultured cells or tissues with RIPA buffer. Proteins were separated by 10% SDS-PAGE and transferred to PVDF membranes (Millipore). Membranes were incubated with specific antibodies. After further incubation with horseradish peroxidase-conjugated secondary antibodies, bound primary antibodies were detected by chemiluminescence (Millipore, USA). The intensity of bands was quantified with ImageJ software (NIH, Bethesda, MD, USA).

Gelatin zymography
Mmp9 activities were determined by a Mmp gelatin zymography kit (GenMed Scientific Inc., USA) according to the manufacturer's instructions. In brief, the supernatant of macrophages culture medium was collected and mixed with sample buffer dye then separated by 10% SDS-PAGE gels containing 0.1% gelatin. The gels were further washed with renaturing buffer and incubated with zymography reaction buffer. Finally, the gels were stained with Coomassie Brilliant Blue.

Microarray gene expression analysis
Total RNA was extracted from peritoneal macrophages in AngII-infused Tsc2 MKO ApoE −/− and Tsc2 WT ApoE −/− mice. The RNA concentration and integrity were determined by Agilent 2100 RNA nano 6000 assay kit (Agilent Technologies, CA, USA). The transcriptome sequencing experiments were performed by BoShang Bio-tech Company (Jinan, China). The differentially expressed genes were identified with a fold-change of >2.0 and P value < 0.05 between two groups.

Statistical analysis
Statistical analysis was performed using SPSS16.0 software. Continuous variables were expressed as mean ± SD if normally distributed or median if non-normally distributed. Categorical data were expressed as frequency and percentage. We tested the normality and equal variance using the Kolmogorov-Smirnov test implemented in Prism statistical software (GraphPad, San Diego) before parametric data analysis. Comparison of 2 groups of normally distributed samples was done using unpaired Student's t tests. Comparison of multiple groups of normally distributed samples was done using ANOVA, and Tukey was performed as a post test. Nonnormally distributed data were analyzed with Mann-Whitney test or the Kruskal-Wallis test, as appropriate. Categorical data were analyzed by chi-square test or Fisher's exact test. P < 0.05 was considered statistically significant.

DATA AVAILABILITY
The experimental data sets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.


Identification and validation of a hypoxia-immune signature for overall survival prediction in lung adenocarcinoma

Objective: The interaction between immunity and hypoxia in tumor microenvironment (TME) has clinical significance, and this study aims to explore immune-hypoxia related biomarkers in LUAD to guide accurate prognosis of patients. Methods: The LUAD gene expression dataset was downloaded from GEO and TCGA databases. The immune-related genes and hypoxia-related genes were acquired from ImmPort and MSigDB databases, respectively. Genes related to immune and hypoxia in LUAD were obtained by intersection. The significantly prognostic genes in LUAD were obtained by LASSO and Cox regression analyses and a prognostic model was constructed. Kaplan-Meier and receiver operating characteristic curves were generated to evaluate and validate model reliability. Single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) were employed to analyze immune cell infiltration and pathway differences between high- and low-risk groups. Nomogram and calibration curves for survival curve and clinical features were drawn to measure prognostic value of the model. Results: The prognosis model of LUAD was constructed based on seven immune-hypoxia related genes: S100P, S100A16, PGK1, TNFSF11, ARRB1, NCR3, and TSLP. Survival analysis revealed a poor prognosis in high-risk group. ssGSEA result suggested that activities of immune cells in high-risk group was remarkably lower than in low-risk group, and GSVA result showed that immune-related pathway was notably activated in low-risk group. Conclusion: Immune-hypoxia related genes were found to be prognostic biomarkers for LUAD patients, based on which a 7-immune-hypoxia related gene-signature was constructed. This model can assess immune status of LUAD patients, and provide clinical reference for individualized prognosis, treatment and follow-up of LUAD patients.

Introduction
Lung cancer is the most frequent fatal disease. According to statistics, 2.09 million new diagnoses and 1.76 million deaths occurred in 2020, and morbidity and mortality are on the rise (Bade and Dela Cruz, 2020). As a histology of non-small cell lung cancer (NSCLC), lung adenocarcinoma (LUAD) with high morbidity and mortality is featured by highly invasive and disruptive growth characteristics (Li et al., 2014). Occurrence, development, recurrence, and patient's prognosis of tumors are associated with pathological type, clinical stage and tumor gene expression (Müller et al., 2016). With the constant development of biotechnology and the rapid development of precision medicine, LUAD-targeted drug research and treatment methods are becoming increasingly mature. Biomarkers identified include EGFR, TP53, AKT1, KRAS and PTEN (Bean et al., 2007;Bleeker et al., 2008;Jin et al., 2010). TP53 mutation is a prevalent mutated gene in LUAD patients and attenuates the immune response of early LUAD patients (Zhu et al., 2021). It is a prognostic biomarker for LUAD patients (Sun et al., 2020). Additionally, phosphorylated AKT1 and MAPK3/1 can co-activate RELA, and they can also activate NF-κB through miR-3613-5p, thus modulating LUAD cell proliferation (He et al., 2020). Currently, these biomarkers have been applied in surgery, targeted therapy, radiotherapy, immunotherapy and chemotherapy. Although the therapeutic effect is evident, only a few patients can benefit from it. In addition, due to local recurrence and remote metastasis, LUAD patients have a 5-year survival rate of 4%-17% (Bade and Dela Cruz, 2020). Thus, to further explore prognostic biomarkers involved in the progression of LUAD is of great significance.
The occurrence and progression of tumor is accompanied by hypoxia, which is one of the markers of TME, and has close relation with the excessive growth, distal metastasis, recurrence and drug resistance of many solid tumors (Rey et al., 2017). Current studies believe that tumor cells under hypoxia can increase expression of hypoxia inducible factor-1 (HIF-1), while overexpressed HIF can be a transcription regulatory factor on target genes. Under the regulation of the transcription level of target genes, many biological processes, including tumor angiogenesis, autophagy, apoptosis, immune regulation, energy metabolism and pH regulation, are affected, which promotes tumor cells to adapt to the hypoxia environment, so that the continuous survival, proliferation and invasion of tumor cells are achieved (Weidemann and Johnson, 2008;Majmundar et al., 2010;Semenza, 2010). Meanwhile, the connection between tumor hypoxia environment and tumor immunity is very close. It has been documented that T cells exhibit immune dysfunction in anoxic microenvironment (Cubillos-Zapata et al., 2017). Specifically, tumors in anoxic environment can induce the expression of CCL28 to recruit a large number of Tregs, thus promoting tumor angiogenesis and tolerance (Facciabene et al., 2011). It is noteworthy that tumor-associated macrophages (TAM) are important components of hypoxic TME, and TAM enhances its inhibitory effect on T cells by expressing HIF-1α (Doedens et al., 2010). Currently, clinical trials have demonstrated that targeting HIF-1α can enhance the viability of immune cells (Sun et al., 2001). These studies suggest that targeting hypoxia pathway can improve tumor immunotherapy, and targeted detection of hypoxia may present patients the benefit of immunotherapy. In conclusion, these findings highlight potential of hypoxia as a prognostic marker for LUAD, and the identification of LUAD prognostic markers from the perspective of hypoxic TME is an important entry point.
In this study, on the basis of gene expression profile of LUAD patients in TCGA database, seven prognostic markers of immune-hypoxia related genes were identified, and a prognostic model was established. TME immune cell infiltration status of patients with varying risk ratings was evaluated to further elucidate the influence of hypoxia environment and immune infiltration on prognoses of LUAD patients, improve understanding of LUAD hypoxic TME, and lay scientific basis for the subsequent study on tumorigenesis, development of LUAD and new targeted therapy.

Construction and assessment of a hypoxia-immune related signature
Based on survival information of LUAD patients, PACKAGE survival (Therneau and Grambsch, 2000) was applied to perform univariate Cox regression analysis on immune-hypoxia related genes in TCGA-LUAD and GSE72094 cohorts, and genes significantly correlated with patient overall survival (OS) were screened (p < 0.01). Intersection of survival-related genes in the two cohorts was taken, and overlapping genes were used for further analysis. Next, TCGA-LUAD was taken as the training set. By using R package glmnet, LASSO Cox regression analysis was done on genes that were remarkably associated with OS (Friedman et al., 2010). Appropriate punishment parameter lambda was selected by the ten-fold cross-validation selection method to remove genes with strong correlation to reduce model complexity. Next, by using R package survival, multivariate Cox regression analysis was conducted on genes screened by LASSO Cox analysis to obtain prognostic genes with immune-hypoxia feature in LUAD. The prognostic risk assessment model of LUAD was built according to risk coefficient and expression of feature genes.

Riskscore
n is the number of selected genes, exp i represents expression of gene i, and β i indicates regression coefficient of gene i.
The riskscore of each cancer patient in TCGA-LUAD cohort was calculated, and patients were assigned into high-or low-risk groups by the median riskscore value. Survival analysis was conducted, and R package survminer was employed to generate survival curves of the two risk groups (https://cran.rproject.org/web/packages/survminer/index.html). The relevant survival state graph was plotted according to the riskcsore of the samples. Subsequently, ROC curves for prognostic models were plotted by R package survival ROC to predict patients' OS at 1, 3 and 5 years (https://rdrr.io/cran/survivalROC/man/ survivalROC.html). Independent data sets GSE31210 and GSE72094 were used as validation sets to verify accuracy of the model. Finally, effects of prognosis-related gene expression on prognoses of LUAD patients were analyzed on the GEPIA website (http://gepia.cancer-pku.cn/index.html).

Single-sample gene set enrichment analysis
Twenty-nine kinds of immune cells and immune-related functional gene sets were got from the published literature (Jiang et al., 2021). We assessed immune cell infiltration level in TME of LUAD patients based on these 29 immune data sets. ssGSEA was on samples in two groups using R package GSVA (Hänzelmann et al., 2013) to study immune cell infiltration level of patients with different riskscores.

Gene set variation analysis
GSVA estimates pathway activation variation in sample populations in an unsupervised manner, which is an excellent molecular mapping method (Hänzelmann et al., 2013). Enrichment scores for varying biological pathways in each sample in TCGA-LUAD cohort were calculated using R package GSVA. From MsigDB (http://software.broadinstitute. org/gsea/msigdb/index.jsp), the "c2.cp.kegg.v7.0.symbols.gmt" data were downloaded as reference. Through GSVA analysis, the differential pathways between groups were obtained. The differential enrichment pathways were further identified, with the standard of FDR < 0.01 and p < 0.01.

Riskcore independence verification and nomogram construction
Univariate and multivariate Cox regression analyses were conducted using riskscore and clinical information (gender, age, T, N, stage) to verify whether riskscore could be an independent prognostic factor for LUAD. To predict 1-, 3-, and 5-year survival rates of LUAD patients, a nomogram was constructed based on riskscore and clinicopathological information using R package rms (Huang et al., 2019). Finally, calibration curves were drawn to evaluate consistency in predicted survival time and actual survival time of patients.

Statistics
All statistical analyses were done on R software (3.3.1). The results were not statistically significant if data did not follow a normal distribution. Differences between groups were compared by Kruskal-Wallis test or Wilcoxon rank-sum test. Cox Regression model was utilized to perform univariate and multivariate analyses. Survival differences were assessed by logarithmic rank test. When p value < 0.01, the data were statistically significant.

Results
Identification of immune-hypoxia-related prognostic genes 1,328 immune-hypoxia-related genes that were overlapped in TCGA-LUAD and GSE72094 were identified ( Figure 1A). Based on these genes as well as the survival information of TCGA-LUAD and GSE72094 cohorts, univariate Cox regression analysis was completed, and 137 and 204 genes associated with survival in the two cohorts were obtained, respectively. By intersecting, 43 genes related to patient survival were identified ( Figure 1B). LASSO Cox regression evaluated the regression coefficients of 43 genes Frontiers in Genetics frontiersin.org Frontiers in Genetics frontiersin.org 04 associated with survival, and a fitting curve was plotted ( Figure 1C). Then, an appropriate punishment parameter lambda was selected according to 10-fold cross-validation. Results showed that when lambda was −4, the model contained 17 survival-related genes, which had the lowest complexity ( Figure 1D). The above 17 genes underwent multivariate Cox regression analysis, and complexity and excellence of different models were evaluated according to Akaike Information Criterion (AIC). The model showing the lowest AIC value was chosen as the prognostic model, which contained seven immune-hypoxiarelated genes significantly related to prognoses of LUAD patients. Among these genes, S100P, S100A16, PGK1 and TNFSF11 were risk factors and ARRB1, NCR3 and TSLP were protective factors ( Figure 1E). As the risk coefficients and expression of seven genes mentioned above, the immune-hypoxia feature prognostic model of LUAD was riskscore = 0.065145908*coef (S100P) − 0.27104694*coef (ARRB1) + 0.104952032*coef (S100A16) + 0.152008632*coef (PGK1) + 0.088969471*coef (TNFSF11) − 0.174725763*coef (NCR3) − 0.056725904 *coef (TSLP). In conclusion, seven immunehypoxia-related genes in LUAD were identified by LASSO and Cox repression analyses combined with patient survival information.

Performance assessment of the prognostic model
Following risk coefficient and expression of 7-gene signature, the riskscore of each LUAD patient in TCGA-LUAD cohort was Frontiers in Genetics frontiersin.org computed, and riskscore distribution map was plotted. Patients were assigned into either high-or low-risk groups (Figure 2A). On the basis of survival time and riskscore of LUAD patients in TCGA-LUAD cohort, we drew a scatter plot of patients' survival time, and the results displayed a negative correlation of survival time and riskscore ( Figure 2B). Meanwhile, we plotted the expression heat maps of the seven feature genes in two risk groups. The heat maps showed that risk factors S100P, S100A16, PGK1 and TNFSF11 expressed highly in highrisk groups, while protective factors ARRB1, NCR3 and TSLP expressed highly in low-risk group ( Figure 2C). Survival curve showed that prognoses of patients in low-risk group was notably better than in high-risk group ( Figure 2D).
Subsequently, ROC curves of 1-, 3-and 5-year survival predicted by the 7-gene signature were plotted. AUC values of 1-, 3-and 5-survival were 0.73, 0.696 and 0.649, respectively ( Figure 2E), suggesting that this prognostic model could evaluate the prognoses of LUAD patients well. Overall, the 7-gene signature presented favorable predictive performance in the training set TCGA-LUAD cohort.
To verify predictive potential of the 7-gene signature, riskscore of patients in GSE72094 and GSE31210 data sets was calculated, and samples were assigned into high-or lowrisk groups. Survival analysis results showed poor OS in high-risk group ( Figure 3A). ROC curves of 1-, 3-and 5-year survival predicted by signature in GSE72094 and GSE31210 data sets were  Figure 3B). In addition, to explore the influence of seven genes on prognoses of LUAD patients, the survival curve was plotted. The results showed that S100P, TNFSF11, ARRB1 and NCR3 directly affected the prognoses of LUAD patients, while S100A16, PGK1 and TSLP did not (Figures 4A-G). In conclusion, this 7-gene signature based on immune-hypoxia related genes was able to accurately predict prognoses of LUAD patients. Frontiers in Genetics frontiersin.org 07

Single-sample gene set enrichment analysis of high-and low-risk groups
To evaluate the differences in TME immune cell infiltration between high-and low-risk patients, ssGSEA was done. Results exhibited that the expression of immunoreactive gene sets, including APC_co_stimulation, APC_co_inhibition, CCR, Check-point, HLA, Cytolytic_activity, Parainflammation, Inflammationpromoting, T_cell_co-stimulation, T_cell_co-inhibition, Type_II_IFN_Response and Type_I_IFN_Response in low-risk group were prominently higher than those in high-risk group (Figure 5A), and the infiltration levels of immune cells B_cells, aDCs, DCs, iDCs, CD8+_T_cells, Mast_cells, Macrophages, Neutrophils, pDC, Tfh, T_helper_cells, TIL, Th1_cells and Treg were higher ( Figure 5B). Based on the above findings, we noted that the immune score of patients in high-risk group was relatively low. Combined with the results of patient survival analysis, we concluded that patients in high-risk group may be immunosuppressed.

Gene set variation analysis results of highand low-risk groups
Based on enrichment score of each sample, we determined differences in enrichment pathways between two risk groups by GSVA (FDR < 0.01, p < 0.01). Heat map indicated that the pathways related to metabolism and mismatch repair, inluding KEGG_ONE_CARBON_POOL_BY_FOLATE, KEGG_ GLYOXYLATE_AND _DICARBOXYLATE_METABOLISM, KEGG_PYRIMIDINE_METABOLISM, KEGG_HOMOLOGOUS_ RECOMBINATION, KEGG_MISMATCH_REPAIR and KEGG_DNA_REPLICATION, were substantially activated in high-risk group. In low-risk group, immune-related pathways, including KEGG_INTESTINAL_IMMUNE _NETWORK_FOR_ IGA_PRODUCTION, KEGG_PRIMARY_IMMUNODEFICIENCY, KEGG_ALLOGRAFT_REJECTION, KEGG_AUTOIMMUNE_ THYROID_DISEASE and KEGG_GRAFT_VERSUS_ HOST_ DISEASE, were significantly activated ( Figure 6). Based on these results, patients in high-risk group may be immunosuppressed.

Validation of riskscore independence
Finally, in TCGA-LUAD data set, univariate and multivariate Cox regression analyses were conducted combining clinical information (gender, age, T, N and Stage) with riskscore. The forest plots suggested that riskscore could be an independent factor of LUAD OS ( Figures 7A,B). Then we integrated the clinical information to generate a nomogram to predict 1-, 3-, and 5-year OS of LUAD patients ( Figure 7C). Finally, nomogram calibration curves presented that the predicted and actual OS values of LUAD patients were highly consistent ( Figures  7D,E). In conclusion, the 7-gene signature could effectively predict prognoses of LUAD patients.

Discussion
It has been confirmed that immune and hypoxic microenvironment are key factors in tumorigenesis and progression. Immunosuppression induced by hypoxia microenvironment is the key factor of tumor immunosuppression (Chen and Mellman, 2013;Kim and Chen, 2016). Therefore, based on hypoxia and immune- Frontiers in Genetics frontiersin.org related genes, we explored LUAD hypoxia-immune microenvironment and established the LUAD prognosis model with 7-immune-hypoxia related genes, which presented favorable performance in predicting prognoses. Herein, seven prognostic biomarkers were identified by Cox regression analysis of immune-hypoxia related genes in LUAD. There were 4 risk factors including S100P, S100A16, PGK1 and TNFSF11, and three protective factors including ARRB1, NCR3 and TSLP. S100P is highly expressed in many cancer tissues, including triple negative breast cancer, and it is related to adverse clinical outcomes such as lymphatic metastasis and tumor growth (Kikuchi et al., 2019). This is consistent with the prognostic trend of S100P on LUAD in this study. Li et al. (2016) manifested that in gallbladder cancer, LASP-1 can arrest cell cycle in G2/M phase by regulating S100P. S100A16 (Fang et al., 2021) and ARRB1 (Zhang et al., 2017) are also related to cell cycle, In this study, high-and low-risk groups differed in degree of enrichment of cell cycle-related pathways. Tumor metastasis is one of the reasons that make cancer uncurable. The occurrence of EMT is accompanied by the occurrence of cancer and reduces cell adhesion and enhances cell migration (Laubli and Borsig, 2019). TNFSF11, also known as RANKL, has been shown by Mineon Park et al. (2021) to substantially improve PC3 cell migration and invasion and enhance EMT after treatment of PC3 cells with RANKL. Meanwhile, ARRB1, S100P, S100A16, and TSLP that are in our established model are also associated with cancer metastasis (Barooei et al., 2015;Zhu et al., 2016;Du et al., 2020;Xu et al., 2020). This is in line with our study that high-and low-risk groups differ in degree of enrichment of cell adhesion-related signaling pathways in GSVA analysis. NCR3 expression is an immune parameter of immune cells in advanced NSCLC (Charrier et al., 2019). The RANK/RANKL signaling in M2 macrophages regulates chemokines production and promotes Treg lymphocytes proliferation, being conducive to immunosuppressive environment (Fujimura et al., 2015). This is also in congruous with our results that the high-and low-risk groups differ in degree of enrichment with immune-related diseases, indicating that the signature genes are related to immunity. Hence, these signature genes are associated with the malignant progression of LUAD and can be applied as prognostic markers for LUAD. Hence, it is reasonable to use the prognostic model constructed by these genes for predicting prognostic risk of LUAD patients.
Immune system is the "scavenger" in the body. In cancer patients, impaired cancer immune circulation leads to antitumor immune deficiency, which is also the cause of cancer occurrence and development (Starzer et al., 2022). We found that ssGSEA scores of HLA, CD8+ T cells, T helper cells and B cells in low-risk group were noticeably higher than in high-risk group. These cytokines and immune cells play important roles in tumor immunity. For example, HLA can present endogenous antigens and activate CD8+ T cells. CD8+ T cells recognizes cancer cells or infected cells, and also activate B cells to produce different types of antibodies to exert the immune function (Rock et al., 2016). T helper cells have rich classifications, in which Tfh cells can help B cells produce antibodies by producing IL-21 and Frontiers in Genetics frontiersin.org expressing Bcl6. Treg cells can modulate the immune response to maintain immune cell homeostasis . In the present study, patients in low-risk group had substantially better prognoses than in the other group, which may be caused by activation of CD8+ T cells in TME by T helper cells and secretion of large amounts of cytokines by B cells. The TME of patients in the low-risk group was in immune activation.
In summary, seven Immune-hypoxia related genes were screened out in LUAD by bioinformatics methods in this study, and a LUAD prognostic assessment model was constructed. Our results showed that this model had good accuracy and universality, and were able to predict clinical outcome of LUAD patients and provide treatment reference. However, deficiencies still exist. Firstly, the data for constructing and verifying the 7-gene model were all retrospective data from

FIGURE 7
Construction and evaluation of the nomogram. (A,B) Univariate and multivariate Cox regression analyses for riskscore and clinicopathological features; (C) The nomogram constructed from riskscore and clinicopathological features was used to predict 1-, 3-, and 5-year survival of patients in the training cohort; (D,E) The calibration curve was used to describe the consistency between nomogram prediction of 3-and 5-year survival and actual survival.
Frontiers in Genetics frontiersin.org TCGA and GEO databases, which may lead to selection bias. In that case, we need to include more prospective data in subsequent studies to further confirm the clinical efficacy of the model and establish our own database for prospective cohort testing, so as to increase the reliability of prognostic signature genes. Secondly, the specific mechanism of 7-gene in LUAD is still unclear, and further experimental exploration is needed. Thirdly, few studies have demonstrated the relationship between NCR3 and LUAD, and thus, studies are warranted to clarify its mechanism. At the same time, we analyzed differential pathways between two LUAD risk groups, supplying a foundation for further exploring interaction mechanism between LUAD hypoxia and immune infiltration.

Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.